In Fig. REF , we compare the optimal and
estimate sum rates of a MIMO system with linear and nonlinear regression
methods. More specifically, we utilize support vector regression (SVR)
with radial basis function (RBF) kernel [1]} for nonlinear
regression. Here, we train on 216 samples and test on 24 samples.
As can be seen from the figure, without proper preprocessing methods
both OLS and SVR fail to fit the data due to nonlinear nature of the
problem. However, the results of the simple OLS, which take feature
design into account, are very close to optimal solutions. The performance
has also proved the feasibility of our approach.
<FIGURE>